Opportunities For Health Monitoring Research Articles

A Wearable Electrochemical Sensor Based on Anti-Fouling and Self-Healing Polypeptide Complex Hydrogels for Sweat Monitoring.

Although continuous monitoring of constituents in complex sweat is crucial for noninvasive physiological evaluation, biofouling on the sweat sensor surface and inadequate flexible self-healing materials restrict its applications. Herein, a fully self-healing and strong anti-biofouling polypeptide complex hydrogel (AuNPs/MoS2/Pep hydrogel) with excellent electrochemical performances was created. The anti-fouling electrochemical sweat sensor was fabricated based on the AuNPs/MoS2/Pep hydrogel to address these issues. It was found that the polypeptide hydrogel was designed to form a network structure and carried abundant hydrophilic groups, resulting in a AuNPs/MoS2/Pep hydrogel with superior anti-biofouling properties in sweat for 30 min and even long-term stability in undiluted human sweat. In addition, SEM, TEM, UV, XPS, and infrared spectrogram demonstrated that the binding force of π-π stacking force between MoS2 and naphthalene groups in the designed peptide endowed the polypeptide complex hydrogel with an excellent self-healing property. Furthermore, the polypeptide complex hydrogel preserved wearable device function of continuously monitoring uric acid (UA) and ascorbic acid (AA) in sweat in situ. This novel fabricated sweat sensor with high anti-biofouling ability, excellent self-healing property, and sensitive and selective analytical capability describes a new opportunity for health monitoring in situ.

The application of machine learning for the prognostics and health management of control element drive system

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation.

Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy. Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component. The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats. Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia. The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.